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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Purposive Learning01:22

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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    本研究介绍了实例意识提示 (IAP) 以改善在多域任务增量学习 (MTIL) 中预训练的视觉语言模型 (PT-VLM). IAP优化了快速设计,减少遗忘并提高了适应新任务的能力.

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    科学领域:

    • 人工智能的人工智能
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 预训练的视觉语言模型 (PT-VLMs) 在多域任务增量学习 (MTIL) 中遇到挑战,特别是前后遗忘,由于任务的增量到来而无法访问先前的数据.
    • 像提示调整一样,参数效率微调 (PEFT) 技术用于调整PT-VLM,但现有的方法忽视了PEFT参数设置的关键作用,特别是提示设计.

    研究的目的:

    • 解决MTIL场景中的各种任务快速设计优化的现有方法的局限性.
    • 提出一个新的框架,即实例感知提示 (IAP),可以增强适应新任务的能力,同时减轻PT-VLM的灾难性遗忘.

    主要方法:

    • 开发了以实例为基础的门式提示 (IA-GP),以在实例级别适应性地在变压器层之间分配提示,改善新任务的适应性并减少遗忘.
    • 引入了以实例为依据的类分布驱动提示 (IA-CDDP),通过计算与任务标签相关的实例级信心分数来提高任务适应性.

    主要成果:

    • 对11个数据集的实验评估证明了拟议的实例意识提示 (IAP) 框架的有效性.
    • 与现有方法相比,IA-GP和IA-CDDP策略在MTIL环境中显著提高了性能,展示了优越的适应性和减少的遗忘.
    • 该方法在三个标准性能指标上取得了强的结果,验证了其有效性.

    结论:

    • 拟议的实例意识提示 (IAP) 框架有效优化PT-VLM在MTIL场景中的提示设计.
    • IA-GP和IA-CDDP提供了一个强大的解决方案,用于增强新任务的适应性和缓解记忆受限制的增量学习设置中的遗忘.
    • 这些发现强调了实例级快速优化对于在动态学习环境中推进PT-VLM能力的重要性.